Objectives The accurate and rapid estimation of landslide volume is a fundamental component in landslide disaster assessment and mitigation efforts. It plays a significant role in understanding the scale and potential risks of landslide disasters, formulating effective prevention and control measures, assessing disaster losses, and enhancing disaster risk reduction capabilities. However, in China, estimating the volume of landslides in remote mountainous regions is highly challenging, and the existing methods are not applicable to emergency situations that require rapid response.
Methods In response to the challenges of conducting field surveys and the insufficient theoretical foundation for landslide volume estimation in China, landslide data from the past 30 a (1995—2024) are collected based on literature reports. We obtain information such as the length, width, area, and volume of the landslides, and use the landslide area-volume power-law relationship and geometric modeling methods to obtain model parameters, enabling the estimation of unknown landslide volumes.
Results We ultimately establish a landslide area-volume power-law relationship model applicable to different regions of China, and geometrically validate and mathematically prove the landslide power-law relationship. Then the variation patterns and ranges of the power-law parameters under different environmental conditions are discussed. Additionally, we discuss and analyze the landslide volume estimation method based on geometric modeling and its improvements, and compare the accuracy of the two methods for landslide volume estimation, and summary the applicability and characteristics of each method.
Conclusions The power-law model can effectively estimate the landslide volume and has good applicability in practical use. On the other hand, the geometric modeling method fully considers the geometric characteristics of the landslide. Compared to previous methods, it requires simpler parameter acquisition, has a solid theoretical basis in empirical formulas, and offers stronger applicability. The research findings provide theoretical support for the estimation of landslide volume and offer important reference for rapid estimation of landslide volume and assessment of landslide hazards.
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